mirror of
https://github.com/blakeblackshear/frigate.git
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264 lines
11 KiB
Python
264 lines
11 KiB
Python
import logging
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import ctypes
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import sys
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from pathlib import Path
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from typing import Literal
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import numpy as np
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from pydantic import Field
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from frigate.const import MODEL_CACHE_DIR
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig
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from frigate.detectors.detector_utils import apply_amd_compatibility_env_vars
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logger = logging.getLogger(__name__)
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DETECTOR_KEY = "migraphx"
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class migraphxDetectorConfig(BaseDetectorConfig):
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type: Literal[DETECTOR_KEY]
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device: str = Field(default="gpu", title="Device Type (gpu or cpu)")
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conserve_cpu: bool = Field(default=True, title="Conserve CPU at the expense of latency")
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fast_math: bool = Field(default=True, title="Optimize math functions to use faster approximate versions")
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exhaustive_tune: bool = Field(default=False, title="Use exhaustive search to find the fastest generated kernels")
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class migraphxDetector(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, detector_config: migraphxDetectorConfig):
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super().__init__(detector_config)
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apply_amd_compatibility_env_vars()
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try:
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sys.path.append("/opt/rocm/lib")
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import migraphx
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logger.info(f"migraphx: loaded migraphx module")
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except ModuleNotFoundError:
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logger.error("migraphx: module loading failed, missing migraphx")
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raise
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assert detector_config.model.path is not None, "No model.path configured, please configure model.path"
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assert detector_config.model.labelmap_path is not None, "No model.labelmap_path configured, please configure model.labelmap_path"
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device = detector_config.device.lower()
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assert device in ["gpu", "cpu"], "Invalid device set, must be gpu (default) or cpu"
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try:
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if device == "gpu":
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if detector_config.conserve_cpu:
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logger.info(f"migraphx: enabling hipDeviceScheduleYield to forcefully conserve CPU (conserve_cpu=true)")
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ctypes.CDLL('/opt/rocm/lib/libamdhip64.so').hipSetDeviceFlags(4)
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else:
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# Default to hipDeviceScheduleAuto
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ctypes.CDLL('/opt/rocm/lib/libamdhip64.so').hipSetDeviceFlags(0)
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except Exception as e:
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logger.warning(f"migraphx: could not set hipSetDeviceFlags: {e}")
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cache_dir = Path(MODEL_CACHE_DIR) / "migraphx"
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cache_dir.mkdir(parents=True, exist_ok=True)
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path = Path(detector_config.model.path)
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filename = path.stem + ('_cpu' if device == 'cpu' else ('_tune' if detector_config.exhaustive_tune else ''))
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mxr_cache_path = cache_dir / f"{filename}.mxr"
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mxr_path = path.parent / f"{filename}.mxr"
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if mxr_cache_path.exists():
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logger.info(f"migraphx: loading compiled model from cache {mxr_cache_path}")
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self.model = migraphx.load(str(mxr_cache_path))
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elif mxr_path.exists():
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logger.info(f"migraphx: loading compiled model from {mxr_path}")
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self.model = migraphx.load(str(mxr_path))
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else:
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logger.info(f"migraphx: loading model from {path}")
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if path.suffix == '.onnx':
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self.model = migraphx.parse_onnx(str(path))
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else:
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raise Exception(f"migraphx: unknown model format {path}")
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self.model_output_shapes = self.model.get_output_shapes()
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self.model_is_nms = self.model_output_shapes[0].lens()[2] <= 7
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assert self.model_is_nms is False, "migraphx does not currently support NMS models"
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logger.info(f"migraphx: compiling the model... (fast_math: {detector_config.fast_math}, exhaustive_tune: {detector_config.exhaustive_tune})")
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self.model.compile(
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migraphx.get_target(device),
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offload_copy=True,
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fast_math=detector_config.fast_math,
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exhaustive_tune=detector_config.exhaustive_tune
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)
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logger.info(f"migraphx: saving compiled model into cache {mxr_cache_path}")
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migraphx.save(self.model, str(mxr_cache_path))
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self.model_param_name = self.model.get_parameter_names()[0]
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self.model_input_shape_obj = self.model.get_parameter_shapes()[self.model_param_name]
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self.model_input_shape = tuple(self.model_input_shape_obj.lens())
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self.model_input_arg = migraphx.generate_argument(self.model_input_shape_obj)
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self.model_input_array = np.frombuffer(self.model_input_arg, dtype=np.float32).reshape(self.model_input_shape)
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logger.info(f"migraphx: model loaded (input: {self.model_input_shape})")
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def preprocess(self, tensor_input):
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# Ensure we have a 4D tensor
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if tensor_input.ndim == 3:
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tensor_input = np.expand_dims(tensor_input, axis=0)
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target_n, target_c, target_h, target_w = self.model_input_shape
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# Ensure input shape is NCHW
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if tensor_input.shape[1] == target_c:
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# Already (N, C, H, W), do nothing
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pass
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elif tensor_input.shape[3] == target_c:
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# From (N, H, W, C) to (N, C, H, W)
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tensor_input = tensor_input.transpose(0, 3, 1, 2)
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elif tensor_input.shape[2] == target_c:
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# From (N, H, C, W) to (N, C, H, W)
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tensor_input = tensor_input.transpose(0, 2, 1, 3)
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np.copyto(self.model_input_array, tensor_input)
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def detect_raw(self, tensor_input):
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self.preprocess(tensor_input)
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outputs = self.model.run({self.model_param_name: self.model_input_arg})
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tensor_output = np.frombuffer(
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outputs[0],
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dtype=np.float32
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).reshape(outputs[0].get_shape().lens())
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return self.optimized_process_yolo(tensor_output)
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def optimized_process_yolo(
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self,
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tensor_output,
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confidence_threshold=0.4,
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intersection_over_union_threshold=0.4,
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top_k=100
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):
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# Transpose the raw output so each row represents one candidate detection
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# Typical YOLO format: [batch, features, candidates] -> [candidates, features]
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candidate_detections = tensor_output[0].T
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# Extract class probability scores
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class_probability_matrix = candidate_detections[:, 4:]
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# Identify the highest class score and its index for every candidate box
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max_class_scores = np.max(class_probability_matrix, axis=1)
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# Create a boolean mask to filter out low-confidence predictions immediately
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confidence_mask = max_class_scores > confidence_threshold
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# Early exit if no detections meet the minimum confidence requirements
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if not np.any(confidence_mask):
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return np.zeros((20, 6), dtype=np.float32)
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# Filter the detections, scores, and class IDs using the boolean mask
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filtered_detections = candidate_detections[confidence_mask]
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filtered_scores = max_class_scores[confidence_mask]
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# Limit detections to top_k, reducing the set
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if len(filtered_scores) > top_k:
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partition_idx = np.argpartition(-filtered_scores, top_k)[:top_k]
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filtered_detections = filtered_detections[partition_idx]
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filtered_scores = filtered_scores[partition_idx]
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# Obtain class IDs of reduced set
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filtered_class_ids = np.argmax(filtered_detections[:, 4:], axis=1)
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# YOLO boxes are typically [center_x, center_y, width, height]
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center_coordinates_and_dimensions = filtered_detections[:, :4]
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# Pre-calculate an inversion scale to convert pixels to 0.0-1.0 range
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# This replaces multiple division operations with a single multiplication later
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normalization_scale_vector = np.array([
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1.0 / self.width,
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1.0 / self.height,
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1.0 / self.width,
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1.0 / self.height
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], dtype=np.float32)
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# Calculate half-widths and half-heights to find box corners
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half_dimensions = center_coordinates_and_dimensions[:, 2:4] * 0.5
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# Initialize a buffer for the corner-format boxes [x1, y1, x2, y2]
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corner_format_boxes = np.empty_like(center_coordinates_and_dimensions)
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# Calculate Top-Left corners (x1, y1)
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corner_format_boxes[:, :2] = (center_coordinates_and_dimensions[:, :2] - half_dimensions)
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# Calculate Bottom-Right corners (x2, y2)
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corner_format_boxes[:, 2:4] = (center_coordinates_and_dimensions[:, :2] + half_dimensions)
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# Transform pixel coordinates into normalized values (0.0 to 1.0)
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normalized_boxes = corner_format_boxes * normalization_scale_vector
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# Apply Non-Maximum Suppression (NMS) to remove redundant, overlapping boxes
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# returns indices of detections to keep
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nms_kept_indices = self.optimized_nms(
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normalized_boxes,
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filtered_scores,
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intersection_over_union_threshold
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)
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# Limit the number of detections to the top 20
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number_of_detections_to_save = min(len(nms_kept_indices), 20)
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top_detection_indices = nms_kept_indices[:number_of_detections_to_save]
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# Pre-allocate a fixed-size result array (20 detections, 6 columns each)
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# Column structure: [class_id, confidence, y1, x1, y2, x2]
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results = np.zeros((20, 6), dtype=np.float32)
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# Bulk assign data into results using the NMS indices
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# We explicitly swap X and Y during assignment to meet the [y1, x1, y2, x2] requirement
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results[:number_of_detections_to_save, 0] = filtered_class_ids[top_detection_indices] # class_id
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results[:number_of_detections_to_save, 1] = filtered_scores[top_detection_indices] # confidence
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# Coordinate Mapping
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results[:number_of_detections_to_save, 2] = normalized_boxes[top_detection_indices, 1] # y1
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results[:number_of_detections_to_save, 3] = normalized_boxes[top_detection_indices, 0] # x1
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results[:number_of_detections_to_save, 4] = normalized_boxes[top_detection_indices, 3] # y2
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results[:number_of_detections_to_save, 5] = normalized_boxes[top_detection_indices, 2] # x2
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return results
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def optimized_nms(self, boxes, scores, iou_threshold):
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x1 = boxes[:, 0]
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y1 = boxes[:, 1]
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x2 = boxes[:, 2]
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y2 = boxes[:, 3]
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areas = (x2 - x1) * (y2 - y1)
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order = scores.argsort()[::-1]
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keep = []
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while order.size > 0:
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i = order[0]
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keep.append(i)
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if order.size == 1: break
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# Calculate intersection
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xx1 = np.maximum(x1[i], x1[order[1:]])
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yy1 = np.maximum(y1[i], y1[order[1:]])
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xx2 = np.minimum(x2[i], x2[order[1:]])
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yy2 = np.minimum(y2[i], y2[order[1:]])
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w = np.maximum(0.0, xx2 - xx1)
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h = np.maximum(0.0, yy2 - yy1)
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inter = w * h
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# IoU = Inter / (Area1 + Area2 - Inter)
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ovr = inter / (areas[i] + areas[order[1:]] - inter)
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# Filter indices
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inds = np.where(ovr <= iou_threshold)[0]
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order = order[inds + 1]
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return keep
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